Anis Elgabli
Papers
1
Total Citations
222
H-Index
1
About
Anis Elgabli is a leading researcher at the intersection of distributed machine learning, wireless communications, and edge intelligence. His work addresses a critical challenge in modern networks: how to train powerful ML models efficiently across distributed devices while respecting communication and privacy constraints. His highly cited 2021 paper, “Communication-efficient and distributed learning over wireless networks: principles and applications” (222 citations), provides a foundational framework for enabling collaborative learning at the network edge—a key enabler for 5G and beyond. Elgabli’s major contributions include designing algorithms that dramatically reduce the communication overhead of distributed learning, making it practical for resource-constrained edge devices. His research has profound implications for applications ranging from autonomous driving to smart healthcare, where real-time, privacy-preserving decision-making is essential. With a growing body of work that bridges theory and practice, Elgabli is shaping the future of intelligent, decentralized systems, and his insights are guiding the next generation of wireless networks toward greater efficiency and autonomy.
Research Focus
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Top Papers
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